Fetching the paper…
Reading the bibliography…
Tuning pre-trained language models (PLMs) with task-specific prompts has been a promising approach for text classification.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
A statistical interpretation of term specificity and its application in retrieval
Karen Sparck Jones. 1972 · 1972
Earlier work this paper cites.
WordNet::Similarity - measuring the relatedness of concepts
Ted Pedersen, Siddharth Patwardhan, and Jason Michelizzi. 2004 · 2004
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text
Julian J. McAuley and Jure Leskovec. 2013 · 2013
Earlier work this paper cites.
Dbpedia–a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, et al. 2015 · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Ms marco: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Earlier work this paper cites.
SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
Earlier work this paper cites.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Earlier work this paper cites.
Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Earlier work this paper cites.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Text classification algorithms: A survey
Kamran Kowsari, Kiana Jafari Meimandi, Mojtaba Heidarysafa, Sanjana Mendu, Laura Barnes, and Donald Brown. 2019 · 2019
Cited alongside, same era.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019 · 2019
Cited alongside, same era.
Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach
Wenpeng Yin, Jamaal Hay, and Dan Roth. 2019 · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Later among the works it cites.
Adaprompt: Adaptive prompt-based finetuning for relation extraction
Xiang Chen, Xin Xie, Ningyu Zhang, Jiahuan Yan, Shumin Deng, Chuanqi Tan, Fei Huang, Luo Si, and Huajun Chen. 2021 · 2021
Closest in time.
Openprompt: An open-source framework for prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun. 2021 · 2021
Closest in time.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Closest in time.
WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Calibrated language model fine-tuning for in- and out-of-distribution data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao, and Chao Zhang. 2020 · 2020
Cited alongside, same era.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
Cited alongside, same era.
Text classification using label names only: A language model self-training approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020 · 2020
Cited alongside, same era.
How context affects language models’ factual predictions
Fabio Petroni, Patrick Lewis, Aleksandra Piktus, Tim Rocktäschel, Yuxiang Wu, Alexander H. Miller, and Sebastian Riedel. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Cited alongside, same era.
Automatically identifying words that can serve as labels for few-shot text classification
Timo Schick, Helmut Schmid, and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Ptr: Prompt tuning with rules for text classification
Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, and Maosong Sun. 2021 · 2021
Closest in time.
Surface form competition: Why the highest probability answer isn’t always right
Ari Holtzman, Peter West, Vered Schwartz, Yejin Choi, and Luke Zettlemoyer. 2021 · 2021
Closest in time.
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2021 · 2021
Closest in time.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021b · 2021
Closest in time.
Pre-trained models: Past, present and future
Han Xu, Zhang Zhengyan, Ding Ning, Gu Yuxian, Liu Xiao, Huo Yuqi, Qiu Jiezhong, Zhang Liang, Han Wentao, Huang Minlie, et al. 2021 · 2021
Closest in time.
Calibrate before use: Improving few-shot performance of language models
Tony Z Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Closest in time.